ABHD6 suppression promotes anti-inflammatory polarization of adipose tissue macrophages via 2-monoacylglycerol/PPAR signaling in obese mice
Bibliographic record
Abstract
OBJECTIVE: Pro-inflammatory polarization of adipose tissue macrophages (ATMs) plays a critical role in the pathogenesis of obesity-associated chronic inflammation. However, little is known about the role of lipids in the regulation of ATMs polarity and inflammation in response to metabolic stress. Deletion of α/β-hydrolase domain-containing 6 (ABHD6), a monoacylglycerol (MAG) hydrolase, has been shown to protect against diet-induced obesity and insulin resistance. METHODS: Here we investigated the immunometabolic role of macrophage ABHD6 in response to nutrient excess using whole-body ABHD6-KO mice and human and murine macrophage cell-lines treated with KT203, a selective and potent pharmacological ABHD6 inhibitor. RESULTS: KO mice on high-fat diet showed lower susceptibility to systemic diet-induced inflammation. Moreover, in the setting of overnutrition, stromal vascular cells from gonadal fat of KO vs. control mice contained lower number of M1 macrophages and exhibited enhanced levels of metabolically activated macrophages (MMe) and M2 markers, oxygen consumption, and interleukin-6 (IL-6) release. Likewise, under in vitro nutri-stress condition, inhibition of ABHD6 in MMe-polarized macrophages attenuated the expression and release of pro-inflammatory cytokines and M1 markers and induced the upregulation of lipid metabolism genes. ABHD6-inhibited MMe macrophages showed elevated levels of peroxisome proliferator-activated receptors (PPARs) and 2-MAG species. Notably, among different MAG species, only 2-MAG treatment led to increased levels of PPAR target genes in MMe macrophages. CONCLUSIONS: Collectively, our findings identify ABHD6 as a key component of pro-inflammatory macrophage activation in response to excess nutrition and implicate an endogenous macrophage lipolysis/ABHD6/2-MAG/PPARs cascade, as a lipid signaling and immunometabolic pathway, which favors the anti-inflammatory polarization of ATMs in obesity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".